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概要と使いどころ
Python library for biology: sequence manipulation (DNA/RNA/protein), pairwise/multiple alignment, phylogenetic trees (NJ, UPGMA), diversity (Shannon, Faith PD, Bray-Curtis, UniFrac), ordination (PCoA, CCA, RDA), stats (PERMANOVA, ANOSIM, Mantel), file I/O (FASTA, FASTQ, Newick, BIOM). Use for microbiome, community ecology, or phylogenetics.
日本語の概要は準備中です。原文の説明を表示しています。
WSI processing for digital pathology. Tissue detection, tile extraction (random, grid, score-based), filter pipelines for H&E/IHC. For dataset prep, tile-based DL, slide QC. Use pathml for multiplexed imaging.
日本語の概要は準備中です。原文の説明を表示しています。
AI-assisted pair programming with multiple modes (driver/navigator/switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Features automatic role switching, continuous code review, security scanning, and performance optimization with truth-score verification.
日本語の概要は準備中です。原文の説明を表示しています。
Provides AWS CloudFormation patterns for CloudFront distributions, origins (ALB, S3, Lambda@Edge, VPC Origins), CacheBehaviors, Functions, SecurityHeaders, parameters, Outputs and cross-stack references. Use when creating CloudFront distributions with CloudFormation, configuring multiple origins, implementing caching strategies, managing custom domains with ACM, configuring WAF, and optimizing performance.
日本語の概要は準備中です。原文の説明を表示しています。
Provides patterns to implement persistence layers with Spring Data JPA. Use when creating repositories, configuring entity relationships, writing queries (derived and `@Query`), setting up pagination, database auditing, transactions, UUID primary keys, multiple databases, and database indexing.
日本語の概要は準備中です。原文の説明を表示しています。
When the user needs to analyze, categorize, or extract actionable insights from customer feedback across multiple sources, especially feature requests.
日本語の概要は準備中です。原文の説明を表示しています。
Generate safety toolbox talks for construction crews. Create contextual safety briefings based on weather, work activities, and recent incidents. Support multiple languages.
日本語の概要は準備中です。原文の説明を表示しています。
Provide data-driven decision support for construction. Analyze multiple factors and recommend optimal project decisions.
日本語の概要は準備中です。原文の説明を表示しています。
Merge pandas DataFrames from multiple construction sources. Handle different schemas, keys, and data quality issues.
日本語の概要は準備中です。原文の説明を表示しています。
Batch convert multiple CAD/BIM files (Revit, IFC, DWG, DGN) with progress tracking, error handling, and consolidated reporting.
日本語の概要は準備中です。原文の説明を表示しています。
Load and parse DDC CWICR construction cost database from multiple formats: Parquet, Excel, CSV, Qdrant snapshots. Foundation for all CWICR operations.
日本語の概要は準備中です。原文の説明を表示しています。
Build cost assemblies from CWICR work items. Combine multiple items into reusable templates for common construction elements.
日本語の概要は準備中です。原文の説明を表示しています。
Optimize shared resource pools across multiple construction projects. Balance resource allocation, minimize conflicts, and maximize utilization across the portfolio.
日本語の概要は準備中です。原文の説明を表示しています。
Orchestrate multiple AI agents for construction workflows: estimator, scheduler, document, QA and safety agents coordinated by a supervisor agent, with human checkpoints. Use when automating end-to-end project processes with agentic AI.
日本語の概要は準備中です。原文の説明を表示しています。
Aggregate and analyze IoT sensor data from construction sites. Collect data from multiple sensor types, detect anomalies, and trigger alerts for safety and quality monitoring.
日本語の概要は準備中です。原文の説明を表示しています。
Comprehensive guide to Spark Structured Streaming for production workloads. Use when building streaming pipelines, working with Kafka ingestion, implementing Real-Time Mode (RTM), configuring triggers (processingTime, availableNow), handling stateful operations with watermarks, optimizing checkpoints, performing stream-stream or stream-static joins, writing to multiple sinks, or tuning streaming cost and performance.
日本語の概要は準備中です。原文の説明を表示しています。
Generate realistic synthetic data using Spark + Faker (strongly recommended). Supports serverless execution, multiple output formats (Parquet/JSON/CSV/Delta), and scales from thousands to millions of rows. For small datasets (<10K rows), can optionally generate locally and upload to volumes. Use when user mentions 'synthetic data', 'test data', 'generate data', 'demo dataset', 'Faker', or 'sample data'.
日本語の概要は準備中です。原文の説明を表示しています。
Auto-generates weekly KPI reports from multiple data sources including Supabase analytics, CRM data, financial spreadsheets, and email metrics. Produces executive-ready reports with dashboards, trends, highlights, concerns, and action items.
日本語の概要は準備中です。原文の説明を表示しています。
Generate sports trivia questions by sport/era/difficulty. Multiple formats with obscure stats, fun facts, pub quiz ready content.
日本語の概要は準備中です。原文の説明を表示しています。
Transform webinar recordings into multiple content assets including blog post series, social media snippets, infographic ideas, email sequences, and sales one-pagers. Extracts key moments, quotes, and insights for maximum content ROI. Use when users need to repurpose webinars, video content, or live presentations into multi-channel marketing materials.
日本語の概要は準備中です。原文の説明を表示しています。
Search multiple free stock photo sites simultaneously. Filter by license type, orientation, and color scheme.
日本語の概要は準備中です。原文の説明を表示しています。
Create realistic play-by-play commentary. Multiple announcer styles: traditional, hyped, analytical, homer. Color commentary included.
日本語の概要は準備中です。原文の説明を表示しています。
CHAIN multiple existing skills into custom multi-step workflows. Programmable skill combinations with automatic handoffs. Create composite skills from building blocks with conditional logic.
日本語の概要は準備中です。原文の説明を表示しています。
Aggregate prospect intelligence from multiple sources including news, social media, company websites, and financial data.
日本語の概要は準備中です。原文の説明を表示しています。